Educational wine recommendations from initially sparse data

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Wineout aims to democratize knowledge and appreciation of wine through the use of technology. Their initialeffort in this area was a game-based app intended for non-expert wine drinkers, but this requires an initialunderstanding of wine characteristics that is beyond the knowledge of most consumers. To further advanceWineout's mission, the present project was formulated between the company and university to design andprototype development of a recommendation system for wines that begins with limited user data, and overtime, becomes tailored to the individual consumer's tastes and profiles of similar users. The objective is notonly to offer recommendations that the user is likely to enjoy, but also to help educate users as to specificcharacteristics of the wines. Much of this project can be viewed as a conventional machine learning challenge,but there is an arguably even more important component that relates to the user experience. Thus, significanteffort will be allocated to gaining an understanding of how the target audience for the app currently makes theirwine selections, and ensuring that the app supports existing habits.